Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations5840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory456.4 KiB
Average record size in memory80.0 B

Variable types

Text1
Numeric8
Categorical4

Alerts

engine is highly overall correlated with max_power and 2 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 2 other fieldsHigh correlation
nm is highly overall correlated with engine and 2 other fieldsHigh correlation
rpm is highly overall correlated with nmHigh correlation
seats is highly overall correlated with engineHigh correlation
transmission is highly overall correlated with max_powerHigh correlation
year is highly overall correlated with km_drivenHigh correlation
seller_type is highly imbalanced (67.7%) Imbalance
transmission is highly imbalanced (57.6%) Imbalance

Reproduction

Analysis started2024-11-27 20:42:52.480259
Analysis finished2024-11-27 20:43:34.800879
Duration42.32 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct1924
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
2024-11-27T20:43:35.355487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length54
Median length42
Mean length25.221918
Min length11

Characters and Unicode

Total characters147296
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique912 ?
Unique (%)15.6%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowMaruti Swift VXI BSIII
5th rowHyundai Xcent 1.2 VTVT E Plus
ValueCountFrequency (%)
maruti 1804
 
6.5%
hyundai 1058
 
3.8%
mahindra 611
 
2.2%
swift 558
 
2.0%
tata 534
 
1.9%
bsiv 494
 
1.8%
diesel 460
 
1.7%
1.2 423
 
1.5%
vxi 409
 
1.5%
plus 401
 
1.5%
Other values (828) 20886
75.6%
2024-11-27T20:43:36.356076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21799
 
14.8%
a 10756
 
7.3%
i 9826
 
6.7%
t 7431
 
5.0%
r 6474
 
4.4%
o 5841
 
4.0%
n 5635
 
3.8%
e 5425
 
3.7%
u 4335
 
2.9%
S 3981
 
2.7%
Other values (58) 65793
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 147296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21799
 
14.8%
a 10756
 
7.3%
i 9826
 
6.7%
t 7431
 
5.0%
r 6474
 
4.4%
o 5841
 
4.0%
n 5635
 
3.8%
e 5425
 
3.7%
u 4335
 
2.9%
S 3981
 
2.7%
Other values (58) 65793
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 147296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21799
 
14.8%
a 10756
 
7.3%
i 9826
 
6.7%
t 7431
 
5.0%
r 6474
 
4.4%
o 5841
 
4.0%
n 5635
 
3.8%
e 5425
 
3.7%
u 4335
 
2.9%
S 3981
 
2.7%
Other values (58) 65793
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 147296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21799
 
14.8%
a 10756
 
7.3%
i 9826
 
6.7%
t 7431
 
5.0%
r 6474
 
4.4%
o 5841
 
4.0%
n 5635
 
3.8%
e 5425
 
3.7%
u 4335
 
2.9%
S 3981
 
2.7%
Other values (58) 65793
44.7%

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4284
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-11-27T20:43:36.701398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0956221
Coefficient of variation (CV)0.0020341533
Kurtosis1.6930209
Mean2013.4284
Median Absolute Deviation (MAD)3
Skewness-1.0195011
Sum11758422
Variance16.774118
MonotonicityNot monotonic
2024-11-27T20:43:37.014130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017 666
11.4%
2016 596
10.2%
2015 568
9.7%
2018 522
8.9%
2012 507
8.7%
2014 480
8.2%
2013 478
8.2%
2011 461
7.9%
2010 322
 
5.5%
2019 298
 
5.1%
Other values (19) 942
16.1%
ValueCountFrequency (%)
1983 1
 
< 0.1%
1991 1
 
< 0.1%
1994 3
 
0.1%
1995 1
 
< 0.1%
1996 3
 
0.1%
1997 10
0.2%
1998 9
0.2%
1999 12
0.2%
2000 19
0.3%
2001 7
 
0.1%
ValueCountFrequency (%)
2020 58
 
1.0%
2019 298
5.1%
2018 522
8.9%
2017 666
11.4%
2016 596
10.2%
2015 568
9.7%
2014 480
8.2%
2013 478
8.2%
2012 507
8.7%
2011 461
7.9%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73952.242
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-11-27T20:43:37.370858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11000
Q139000
median70000
Q3100000
95-th percentile156041
Maximum2360457
Range2360456
Interquartile range (IQR)61000

Descriptive statistics

Standard deviation60071.137
Coefficient of variation (CV)0.81229635
Kurtosis416.02466
Mean73952.242
Median Absolute Deviation (MAD)30000
Skewness12.645506
Sum4.318811 × 108
Variance3.6085414 × 109
MonotonicityNot monotonic
2024-11-27T20:43:37.715187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 429
 
7.3%
80000 356
 
6.1%
70000 350
 
6.0%
60000 327
 
5.6%
50000 305
 
5.2%
100000 279
 
4.8%
90000 262
 
4.5%
40000 227
 
3.9%
110000 225
 
3.9%
30000 184
 
3.2%
Other values (817) 2896
49.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 5
0.1%
1300 1
 
< 0.1%
1303 1
 
< 0.1%
1500 2
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 6
0.1%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
370000 1
< 0.1%

fuel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
Diesel
3177 
Petrol
2579 
CNG
 
50
LPG
 
34

Length

Max length6
Median length6
Mean length5.9568493
Min length3

Characters and Unicode

Total characters34788
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 3177
54.4%
Petrol 2579
44.2%
CNG 50
 
0.9%
LPG 34
 
0.6%

Length

2024-11-27T20:43:38.056035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:43:38.322761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3177
54.4%
petrol 2579
44.2%
cng 50
 
0.9%
lpg 34
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 8933
25.7%
l 5756
16.5%
D 3177
 
9.1%
i 3177
 
9.1%
s 3177
 
9.1%
P 2613
 
7.5%
t 2579
 
7.4%
r 2579
 
7.4%
o 2579
 
7.4%
G 84
 
0.2%
Other values (3) 134
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8933
25.7%
l 5756
16.5%
D 3177
 
9.1%
i 3177
 
9.1%
s 3177
 
9.1%
P 2613
 
7.5%
t 2579
 
7.4%
r 2579
 
7.4%
o 2579
 
7.4%
G 84
 
0.2%
Other values (3) 134
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8933
25.7%
l 5756
16.5%
D 3177
 
9.1%
i 3177
 
9.1%
s 3177
 
9.1%
P 2613
 
7.5%
t 2579
 
7.4%
r 2579
 
7.4%
o 2579
 
7.4%
G 84
 
0.2%
Other values (3) 134
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8933
25.7%
l 5756
16.5%
D 3177
 
9.1%
i 3177
 
9.1%
s 3177
 
9.1%
P 2613
 
7.5%
t 2579
 
7.4%
r 2579
 
7.4%
o 2579
 
7.4%
G 84
 
0.2%
Other values (3) 134
 
0.4%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
Individual
5223 
Dealer
592 
Trustmark Dealer
 
25

Length

Max length16
Median length10
Mean length9.6202055
Min length6

Characters and Unicode

Total characters56182
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 5223
89.4%
Dealer 592
 
10.1%
Trustmark Dealer 25
 
0.4%

Length

2024-11-27T20:43:38.616750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:43:38.843728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
individual 5223
89.1%
dealer 617
 
10.5%
trustmark 25
 
0.4%

Most occurring characters

ValueCountFrequency (%)
d 10446
18.6%
i 10446
18.6%
a 5865
10.4%
l 5840
10.4%
u 5248
9.3%
I 5223
9.3%
v 5223
9.3%
n 5223
9.3%
e 1234
 
2.2%
r 667
 
1.2%
Other values (7) 767
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 10446
18.6%
i 10446
18.6%
a 5865
10.4%
l 5840
10.4%
u 5248
9.3%
I 5223
9.3%
v 5223
9.3%
n 5223
9.3%
e 1234
 
2.2%
r 667
 
1.2%
Other values (7) 767
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 10446
18.6%
i 10446
18.6%
a 5865
10.4%
l 5840
10.4%
u 5248
9.3%
I 5223
9.3%
v 5223
9.3%
n 5223
9.3%
e 1234
 
2.2%
r 667
 
1.2%
Other values (7) 767
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 10446
18.6%
i 10446
18.6%
a 5865
10.4%
l 5840
10.4%
u 5248
9.3%
I 5223
9.3%
v 5223
9.3%
n 5223
9.3%
e 1234
 
2.2%
r 667
 
1.2%
Other values (7) 767
 
1.4%

transmission
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
Manual
5336 
Automatic
 
504

Length

Max length9
Median length6
Mean length6.2589041
Min length6

Characters and Unicode

Total characters36552
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 5336
91.4%
Automatic 504
 
8.6%

Length

2024-11-27T20:43:39.102657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:43:39.331948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 5336
91.4%
automatic 504
 
8.6%

Most occurring characters

ValueCountFrequency (%)
a 11176
30.6%
u 5840
16.0%
M 5336
14.6%
n 5336
14.6%
l 5336
14.6%
t 1008
 
2.8%
A 504
 
1.4%
o 504
 
1.4%
m 504
 
1.4%
i 504
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11176
30.6%
u 5840
16.0%
M 5336
14.6%
n 5336
14.6%
l 5336
14.6%
t 1008
 
2.8%
A 504
 
1.4%
o 504
 
1.4%
m 504
 
1.4%
i 504
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11176
30.6%
u 5840
16.0%
M 5336
14.6%
n 5336
14.6%
l 5336
14.6%
t 1008
 
2.8%
A 504
 
1.4%
o 504
 
1.4%
m 504
 
1.4%
i 504
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11176
30.6%
u 5840
16.0%
M 5336
14.6%
n 5336
14.6%
l 5336
14.6%
t 1008
 
2.8%
A 504
 
1.4%
o 504
 
1.4%
m 504
 
1.4%
i 504
 
1.4%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
First Owner
3603 
Second Owner
1639 
Third Owner
454 
Fourth & Above Owner
 
140
Test Drive Car
 
4

Length

Max length20
Median length11
Mean length11.498459
Min length11

Characters and Unicode

Total characters67151
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner 3603
61.7%
Second Owner 1639
28.1%
Third Owner 454
 
7.8%
Fourth & Above Owner 140
 
2.4%
Test Drive Car 4
 
0.1%

Length

2024-11-27T20:43:39.585769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:43:39.831576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
owner 5836
48.8%
first 3603
30.1%
second 1639
 
13.7%
third 454
 
3.8%
fourth 140
 
1.2%
140
 
1.2%
above 140
 
1.2%
test 4
 
< 0.1%
drive 4
 
< 0.1%
car 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 10041
15.0%
e 7623
11.4%
n 7475
11.1%
6124
9.1%
O 5836
8.7%
w 5836
8.7%
i 4061
6.0%
t 3747
 
5.6%
F 3743
 
5.6%
s 3607
 
5.4%
Other values (14) 9058
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10041
15.0%
e 7623
11.4%
n 7475
11.1%
6124
9.1%
O 5836
8.7%
w 5836
8.7%
i 4061
6.0%
t 3747
 
5.6%
F 3743
 
5.6%
s 3607
 
5.4%
Other values (14) 9058
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10041
15.0%
e 7623
11.4%
n 7475
11.1%
6124
9.1%
O 5836
8.7%
w 5836
8.7%
i 4061
6.0%
t 3747
 
5.6%
F 3743
 
5.6%
s 3607
 
5.4%
Other values (14) 9058
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10041
15.0%
e 7623
11.4%
n 7475
11.1%
6124
9.1%
O 5836
8.7%
w 5836
8.7%
i 4061
6.0%
t 3747
 
5.6%
F 3743
 
5.6%
s 3607
 
5.4%
Other values (14) 9058
13.5%

mileage
Real number (ℝ)

Distinct375
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.428611
Minimum0
Maximum42
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-11-27T20:43:40.125163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.8285
Q116.950001
median19.299999
Q322.299999
95-th percentile25.799999
Maximum42
Range42
Interquartile range (IQR)5.3499985

Descriptive statistics

Standard deviation3.986026
Coefficient of variation (CV)0.20516268
Kurtosis0.87149566
Mean19.428611
Median Absolute Deviation (MAD)2.6999989
Skewness-0.17337286
Sum113463.09
Variance15.888404
MonotonicityNot monotonic
2024-11-27T20:43:40.469631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.29999924 240
 
4.1%
18.89999962 175
 
3.0%
19.70000076 137
 
2.3%
18.60000038 126
 
2.2%
21.10000038 118
 
2.0%
17 105
 
1.8%
15.96000004 95
 
1.6%
17.79999924 90
 
1.5%
16.10000038 84
 
1.4%
15.10000038 76
 
1.3%
Other values (365) 4594
78.7%
ValueCountFrequency (%)
0 14
0.2%
9 4
 
0.1%
9.5 1
 
< 0.1%
10 2
 
< 0.1%
10.10000038 2
 
< 0.1%
10.5 14
0.2%
10.71000004 1
 
< 0.1%
10.75 1
 
< 0.1%
10.80000019 1
 
< 0.1%
10.89999962 4
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.43999863 2
 
< 0.1%
33 1
 
< 0.1%
32.52000046 1
 
< 0.1%
30.45999908 2
 
< 0.1%
28.39999962 70
1.2%
28.09000015 29
0.5%
27.62000084 5
 
0.1%
27.39999962 4
 
0.1%
27.38999939 20
 
0.3%

engine
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1429.449
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2024-11-27T20:43:40.783467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31498
95-th percentile2499
Maximum3604
Range2980
Interquartile range (IQR)301

Descriptive statistics

Standard deviation485.66283
Coefficient of variation (CV)0.33975528
Kurtosis1.1206263
Mean1429.449
Median Absolute Deviation (MAD)213
Skewness1.2669542
Sum8347982
Variance235868.39
MonotonicityNot monotonic
2024-11-27T20:43:41.100174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 944
 
16.2%
1197 589
 
10.1%
796 338
 
5.8%
998 335
 
5.7%
2179 283
 
4.8%
1498 283
 
4.8%
1396 230
 
3.9%
1199 163
 
2.8%
2523 156
 
2.7%
1461 146
 
2.5%
Other values (110) 2373
40.6%
ValueCountFrequency (%)
624 15
 
0.3%
793 5
 
0.1%
796 338
5.8%
799 56
 
1.0%
814 87
 
1.5%
909 2
 
< 0.1%
936 27
 
0.5%
993 24
 
0.4%
995 39
 
0.7%
998 335
5.7%
ValueCountFrequency (%)
3604 1
 
< 0.1%
3498 1
 
< 0.1%
3198 2
 
< 0.1%
2999 2
 
< 0.1%
2997 2
 
< 0.1%
2993 12
0.2%
2987 8
 
0.1%
2982 23
0.4%
2967 8
 
0.1%
2956 14
0.2%

max_power
Real number (ℝ)

High correlation 

Distinct313
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.910782
Minimum0
Maximum400
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-11-27T20:43:41.441564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.299999
Q168
median81.860001
Q399
95-th percentile147.89999
Maximum400
Range400
Interquartile range (IQR)31

Descriptive statistics

Standard deviation31.639585
Coefficient of variation (CV)0.35990563
Kurtosis6.0466399
Mean87.910782
Median Absolute Deviation (MAD)14.809998
Skewness1.7994332
Sum513398.97
Variance1001.0634
MonotonicityNot monotonic
2024-11-27T20:43:41.991751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 271
 
4.6%
74 260
 
4.5%
88.5 163
 
2.8%
67 130
 
2.2%
46.29999924 127
 
2.2%
67.09999847 117
 
2.0%
62.09999847 116
 
2.0%
67.04000092 115
 
2.0%
81.80000305 115
 
2.0%
70 110
 
1.9%
Other values (303) 4316
73.9%
ValueCountFrequency (%)
0 3
 
0.1%
32.79999924 2
 
< 0.1%
34.20000076 17
 
0.3%
35 14
 
0.2%
35.5 2
 
< 0.1%
37 69
1.2%
37.47999954 7
 
0.1%
37.5 6
 
0.1%
38 1
 
< 0.1%
38.40000153 2
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
282 1
 
< 0.1%
280 1
 
< 0.1%
272 1
 
< 0.1%
270.8999939 3
0.1%
265 1
 
< 0.1%
261.3999939 4
0.1%
258 2
< 0.1%
254.8000031 3
0.1%
254.7899933 1
 
< 0.1%

seats
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4267123
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2024-11-27T20:43:42.389643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98234395
Coefficient of variation (CV)0.18102009
Kurtosis4.0793845
Mean5.4267123
Median Absolute Deviation (MAD)0
Skewness2.0098753
Sum31692
Variance0.96499964
MonotonicityNot monotonic
2024-11-27T20:43:42.771833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 4618
79.1%
7 800
 
13.7%
8 191
 
3.3%
4 94
 
1.6%
9 68
 
1.2%
6 48
 
0.8%
10 18
 
0.3%
2 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
2 2
 
< 0.1%
4 94
 
1.6%
5 4618
79.1%
6 48
 
0.8%
7 800
 
13.7%
8 191
 
3.3%
9 68
 
1.2%
10 18
 
0.3%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 18
 
0.3%
9 68
 
1.2%
8 191
 
3.3%
7 800
 
13.7%
6 48
 
0.8%
5 4618
79.1%
4 94
 
1.6%
2 2
 
< 0.1%

nm
Real number (ℝ)

High correlation 

Distinct181
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.38966
Minimum51
Maximum789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-11-27T20:43:43.258944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile69
Q1113
median171
Q3200
95-th percentile330
Maximum789
Range738
Interquartile range (IQR)87

Descriptive statistics

Standard deviation82.179443
Coefficient of variation (CV)0.47670751
Kurtosis4.4586191
Mean172.38966
Median Absolute Deviation (MAD)56.269997
Skewness1.4811313
Sum1006755.6
Variance6753.4604
MonotonicityNot monotonic
2024-11-27T20:43:43.821852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171 622
 
10.7%
200 503
 
8.6%
190 454
 
7.8%
90 297
 
5.1%
114 191
 
3.3%
113 148
 
2.5%
160 139
 
2.4%
62 130
 
2.2%
250 109
 
1.9%
330 101
 
1.7%
Other values (171) 3146
53.9%
ValueCountFrequency (%)
51 13
 
0.2%
57 2
 
< 0.1%
59 85
1.5%
60 2
 
< 0.1%
62 130
2.2%
69 101
1.7%
71 3
 
0.1%
72 56
1.0%
72.90000153 1
 
< 0.1%
74.5 87
1.5%
ValueCountFrequency (%)
789 3
0.1%
640 1
 
< 0.1%
620 6
0.1%
619 3
0.1%
600 3
0.1%
580 2
 
< 0.1%
560 2
 
< 0.1%
550 6
0.1%
540 1
 
< 0.1%
510 1
 
< 0.1%

rpm
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3022.2274
Minimum0
Maximum5300
Zeros23
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2024-11-27T20:43:44.382563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1750
Q12400
median3000
Q33750
95-th percentile4500
Maximum5300
Range5300
Interquartile range (IQR)1350

Descriptive statistics

Standard deviation883.703
Coefficient of variation (CV)0.29240123
Kurtosis-0.47445929
Mean3022.2274
Median Absolute Deviation (MAD)600
Skewness0.025050312
Sum17649808
Variance780931
MonotonicityNot monotonic
2024-11-27T20:43:44.908499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
3000 990
17.0%
4000 707
12.1%
2000 621
10.6%
3500 547
9.4%
1750 430
 
7.4%
2500 398
 
6.8%
2750 328
 
5.6%
2800 310
 
5.3%
4200 183
 
3.1%
4500 162
 
2.8%
Other values (37) 1164
19.9%
ValueCountFrequency (%)
0 23
 
0.4%
500 12
 
0.2%
1400 2
 
< 0.1%
1500 64
 
1.1%
1600 1
 
< 0.1%
1740 1
 
< 0.1%
1750 430
7.4%
1800 29
 
0.5%
1850 1
 
< 0.1%
1900 30
 
0.5%
ValueCountFrequency (%)
5300 1
 
< 0.1%
5200 1
 
< 0.1%
5000 22
 
0.4%
4850 18
 
0.3%
4800 72
1.2%
4750 7
 
0.1%
4700 7
 
0.1%
4600 50
 
0.9%
4500 162
2.8%
4400 55
 
0.9%

Interactions

2024-11-27T20:43:28.936264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:42:54.348092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:02.504437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:06.464628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:11.222264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:17.861522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:20.859434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:24.142226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:29.611470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:42:54.995248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:02.981111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:07.105763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:12.519247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:18.315191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:21.302826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:24.657326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:30.854589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:42:55.698902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:03.402228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:07.579302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:13.525373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:18.685974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:21.658447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:25.236849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:31.850397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:42:59.433745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:03.864254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:08.090868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:14.184763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:19.039342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:21.942824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:25.646419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:32.166505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:00.187084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:04.383002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:08.473690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:15.246978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:19.453721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:22.351327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:26.038381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:32.409305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:00.743003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:04.970091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:09.115283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:16.372405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:19.868326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:22.752559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:26.446665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:33.478492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:01.221650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:05.335036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:09.718719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:16.936863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:20.206895image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:23.241423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:27.079379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:33.724284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:02.001113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:05.902090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:10.505136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:17.502147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:20.572633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:23.697723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T20:43:28.100340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-27T20:43:45.290497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileagenmownerrpmseatsseller_typetransmissionyear
engine1.0000.4450.3050.714-0.4300.8150.078-0.3630.5280.0960.400-0.038
fuel0.4451.0000.0380.1530.2930.4670.0250.4540.2140.0480.0360.133
km_driven0.3050.0381.0000.043-0.1980.2380.033-0.3120.1950.0000.016-0.570
max_power0.7140.1530.0431.000-0.3080.7430.070-0.0530.3050.1550.5130.162
mileage-0.4300.293-0.198-0.3081.000-0.1450.094-0.173-0.4350.0400.2440.347
nm0.8150.4670.2380.743-0.1451.0000.090-0.5650.4280.1350.4550.107
owner0.0780.0250.0330.0700.0940.0901.0000.0960.0160.1340.1190.257
rpm-0.3630.454-0.312-0.053-0.173-0.5650.0961.000-0.1990.0690.1200.076
seats0.5280.2140.1950.305-0.4350.4280.016-0.1991.0000.0220.0340.048
seller_type0.0960.0480.0000.1550.0400.1350.1340.0690.0221.0000.2110.104
transmission0.4000.0360.0160.5130.2440.4550.1190.1200.0340.2111.0000.153
year-0.0380.133-0.5700.1620.3470.1070.2570.0760.0480.1040.1531.000

Missing values

2024-11-27T20:43:34.094599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T20:43:34.577025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearkm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatsnmrpm
0Maruti Swift Dzire VDI2014.0145500.0DieselIndividualManualFirst Owner23.400000124874.0000005190.002000.0
1Skoda Rapid 1.5 TDI Ambition2014.0120000.0DieselIndividualManualSecond Owner21.1399991498103.5199975250.002500.0
2Hyundai i20 Sportz Diesel2010.0127000.0DieselIndividualManualFirst Owner23.000000139690.0000005171.002750.0
3Maruti Swift VXI BSIII2007.0120000.0PetrolIndividualManualFirst Owner16.100000129888.1999975171.003000.0
4Hyundai Xcent 1.2 VTVT E Plus2017.045000.0PetrolIndividualManualFirst Owner20.139999119781.8600015113.754000.0
5Maruti Wagon R LXI DUO BSIII2007.0175000.0LPGIndividualManualFirst Owner17.299999106157.5000005171.003000.0
6Maruti 800 DX BSII2001.05000.0PetrolIndividualManualSecond Owner16.10000079637.000000459.002500.0
7Toyota Etios VXD2011.090000.0DieselIndividualManualFirst Owner23.590000136467.0999985170.002400.0
8Ford Figo Diesel Celebration Edition2013.0169000.0DieselIndividualManualFirst Owner20.000000139968.0999985160.002000.0
9Renault Duster 110PS Diesel RxL2014.068000.0DieselIndividualManualSecond Owner19.0100001461108.4499975248.002250.0
nameyearkm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatsnmrpm
5830Maruti Alto LXi2011.073000.0PetrolIndividualManualFirst Owner19.70000179646.299999562.0000003000.0
5831Maruti 800 AC1997.0120000.0PetrolIndividualManualFirst Owner16.10000079637.000000459.0000002500.0
5832Maruti Alto K10 VXI Airbag2017.045000.0PetrolIndividualManualFirst Owner23.95000199867.099998590.0000003500.0
5833Hyundai i20 Magna2013.025000.0PetrolIndividualManualFirst Owner18.500000119782.8499985113.6999974000.0
5834Maruti Wagon R LXI Optional2017.080000.0PetrolIndividualManualFirst Owner20.51000099867.040001590.0000003500.0
5835Hyundai Santro Xing GLS2008.0191000.0PetrolIndividualManualFirst Owner17.920000108662.099998596.0999983000.0
5836Maruti Wagon R VXI BS IV with ABS2013.050000.0PetrolIndividualManualSecond Owner18.90000099867.099998590.0000003500.0
5837Hyundai i20 Magna2013.0110000.0PetrolIndividualManualFirst Owner18.500000119782.8499985113.6999974000.0
5838Hyundai Verna CRDi SX2007.0119000.0DieselIndividualManualFourth & Above Owner16.7999991493110.0000005171.0000003000.0
5839Maruti Swift Dzire ZDi2009.0120000.0DieselIndividualManualFirst Owner19.299999124873.9000025190.0000002000.0